mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
145 lines
5.0 KiB
Python
145 lines
5.0 KiB
Python
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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import ast
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import csv
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import os
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from pathlib import Path
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from typing import List, Optional
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import numpy as np
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import torch
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import tensorrt_llm
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from tensorrt_llm.logger import logger
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from tensorrt_llm.runtime import ModelRunnerCpp
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from transformers import AutoTokenizer
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def parse_arguments(args=None):
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--input_text',
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type=str,
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nargs='+',
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default=["Born in north-east France, Soyer trained as a"])
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parser.add_argument('--tokenizer_dir', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct")
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parser.add_argument('--engine_dir', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct")
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parser.add_argument('--log_level', type=str, default="debug")
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parser.add_argument('--kv_cache_free_gpu_memory_fraction', type=float, default=0.6)
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parser.add_argument('--temperature', type=float, default=0.8)
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parser.add_argument('--top_k', type=int, default=50)
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parser.add_argument('--top_p', type=float, default=0.95)
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return parser.parse_args(args=args)
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def parse_input(tokenizer,
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input_text=None,
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prompt_template=None):
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batch_input_ids = []
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for curr_text in input_text:
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if prompt_template is not None:
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curr_text = prompt_template.format(input_text=curr_text)
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input_ids = tokenizer.encode(
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curr_text)
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batch_input_ids.append(input_ids)
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batch_input_ids = [
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torch.tensor(x, dtype=torch.int32) for x in batch_input_ids
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]
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logger.debug(f"Input token ids (batch_size = {len(batch_input_ids)}):")
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for i, input_ids in enumerate(batch_input_ids):
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logger.debug(f"Request {i}: {input_ids.tolist()}")
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return batch_input_ids
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def main(args):
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runtime_rank = tensorrt_llm.mpi_rank()
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logger.set_level(args.log_level)
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_dir)
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prompt_template = "<|sos|>{input_text}<|task_id|>"
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end_id = tokenizer.convert_tokens_to_ids("<|eos1|>")
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batch_input_ids = parse_input(tokenizer=tokenizer,
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input_text=args.input_text,
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prompt_template=prompt_template)
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input_lengths = [x.size(0) for x in batch_input_ids]
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runner_kwargs = dict(
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engine_dir=args.engine_dir,
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rank=runtime_rank,
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max_output_len=1024,
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enable_context_fmha_fp32_acc=False,
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max_batch_size=len(batch_input_ids),
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max_input_len=max(input_lengths),
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kv_cache_free_gpu_memory_fraction=args.kv_cache_free_gpu_memory_fraction,
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cuda_graph_mode=False,
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gather_generation_logits=False,
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)
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runner = ModelRunnerCpp.from_dir(**runner_kwargs)
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with torch.no_grad():
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outputs = runner.generate(
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batch_input_ids=batch_input_ids,
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max_new_tokens=1024,
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end_id=end_id,
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pad_id=end_id,
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temperature=args.temperature,
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top_k=args.top_k,
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top_p=args.top_p,
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num_return_sequences=1,
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repetition_penalty=1.1,
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random_seed=42,
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streaming=False,
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output_sequence_lengths=True,
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output_generation_logits=False,
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return_dict=True,
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return_all_generated_tokens=False)
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torch.cuda.synchronize()
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output_ids, sequence_lengths = outputs["output_ids"], outputs["sequence_lengths"]
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num_output_sents, num_beams, _ = output_ids.size()
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assert num_beams == 1
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beam = 0
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batch_size = len(input_lengths)
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num_return_sequences = num_output_sents // batch_size
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assert num_return_sequences == 1
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for i in range(batch_size * num_return_sequences):
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batch_idx = i // num_return_sequences
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seq_idx = i % num_return_sequences
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inputs = output_ids[i][0][:input_lengths[batch_idx]].tolist()
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input_text = tokenizer.decode(inputs)
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print(f'Input [Text {batch_idx}]: \"{input_text}\"')
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output_begin = input_lengths[batch_idx]
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output_end = sequence_lengths[i][beam]
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outputs = output_ids[i][beam][output_begin:output_end].tolist()
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output_text = tokenizer.decode(outputs)
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print(f'Output [Text {batch_idx}]: \"{output_text}\"')
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logger.debug(str(outputs))
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if __name__ == '__main__':
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args = parse_arguments()
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main(args)
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